{"id":"W3200826440","doi":"10.1002/adfm.202104195","title":"Applied Machine Learning for Developing Next‐Generation Functional Materials","year":2021,"lang":"en","type":"article","venue":"Advanced Functional Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Key (lock); Data science; Nanotechnology; Perspective (graphical); Range (aeronautics); Systems engineering; Materials science; Biochemical engineering; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001737864,0.0005099037,0.0006953431,0.0001509929,0.001053267,0.0008837509,0.0003119129,0.0002071782,0.01327596],"category_scores_gemma":[0.001099521,0.0005017524,0.00009543163,0.0003263723,0.0001454111,0.0008840631,0.0002685302,0.0001474545,0.0008239729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002256222,"about_ca_system_score_gemma":0.000371617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002737836,"about_ca_topic_score_gemma":0.00001574534,"domain_scores_codex":[0.9957583,0.000387608,0.001071927,0.001247922,0.0007454507,0.0007888041],"domain_scores_gemma":[0.9978666,0.000380119,0.0005776174,0.0004550534,0.000584858,0.0001357655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003982943,0.00004694845,0.00002632895,0.0001196188,0.00001922559,0.000006084129,0.00004302158,0.01725523,0.9597555,0.02144549,0.0004553848,0.0004288766],"study_design_scores_gemma":[0.001166528,0.00008368008,0.001165633,0.0000501348,0.0000351147,0.00008085304,0.00005378016,0.0004749334,0.9847047,0.002786537,0.008788527,0.000609632],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8542033,0.0001467835,0.1328656,0.0006547167,0.01045096,0.0006346307,0.000248255,0.0004472452,0.0003484481],"genre_scores_gemma":[0.920793,0.00004556968,0.07068966,0.0009425193,0.002153637,0.0006896656,0.002617347,0.000103045,0.001965579],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06658966,"threshold_uncertainty_score":0.999954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04972178018947698,"score_gpt":0.2698919195457412,"score_spread":0.2201701393562642,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}